A simple metric of landscape fire exposure
Bibliographic record
Abstract
Abstract Context Proximity of landcover elements to each other will enable or constrain fire spread. Assessments of potential fire propagation across landscapes typically involve empirical or simulation models that estimate probabilities based on complex interactions among biotic and abiotic controls. Objectives We developed a metric of landscape fire exposure based solely on a grid cell’s proximity to nearby hazardous fuel capable of transmitting fire to its location. To evaluate accuracy of this new metric, we asked:Do burned areas occur preferentially in locations with high exposure? Methods We mapped exposure to hazardous fuels in Alberta, Canada using a neighbourhood analysis. Correspondence between exposure and 2331 fires that burned 2,606,387 ha following our 2007 assessment was evaluated and exposure changes between 2007 and 2019 were assessed. Results In all eleven ecological units analysed, burned area surpluses occurred where exposure was ≥ 60% and corresponding deficits occurred where exposure was < 40%. In seven ecological units, the majority of burned areas had pre-fire exposure ≥ 80%. Between 2007 and 2019, land area with exposure ≥ 80% increased by almost a third. Conclusions Exposure to hazardous fuels is easily quantified with a single thematic layer and aligns well with subsequent fires in Boreal, Foothills and Rocky Mountain natural regions. The resulting fire exposure metric is a numeric rating of the potential for fire transmission to a location given surrounding fuel composition and configuration, irrespective of weather or other fire controls. Exposure can be compared across geographic regions and time periods; and used in conjunction with other metrics of fire controls to inform the study of landscape fire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".